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Calibrating a traffic flow model with parallel differential evolution

Strofylas Giorgos, Porfyri Kalliroi, Nikolos Ioannis, Delis Anargyros, Papageorgiou Markos

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URIhttp://purl.tuc.gr/dl/dias/769CF98D-026C-4DBE-920A-774D0DB071C2-
Identifierhttps://www.researchgate.net/publication/318495057_Calibrating_a_traffic_flow_model_with_parallel_differential_evolution-
Identifierhttps://doi.org/10.4203/ccp.111.26-
Languageen-
Extent20 pagesen
TitleCalibrating a traffic flow model with parallel differential evolutionen
CreatorStrofylas Giorgosen
CreatorΣτροφυλας Γιωργοςel
CreatorPorfyri Kalliroien
CreatorΠορφυρη Καλλιρροηel
CreatorNikolos Ioannisen
CreatorΝικολος Ιωαννηςel
CreatorDelis Anargyrosen
CreatorΔελης Αναργυροςel
CreatorPapageorgiou Markosen
CreatorΠαπαγεωργιου Μαρκοςel
PublisherCivil-Comp Pressen
DescriptionThe research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 321132, project TRAMAN21. en
Content SummaryGiven the importance of the credibility and validity required in macroscopic traffic flow models while performing real-word simulations, the necessity of employing an efficient, computationally fast, and reliable constrained optimization scheme for model calibration appears to be mandatory to ensure that the traffic flow characteristics are accurately represented by such models. To this end, a parallel, metamodel-assisted Differential Evolution (DE) algorithm is employed for the calibration of the second-order macroscopic gas-kinetic traffic flow (GKT) model using real traffic data from Attiki Odos freeway in Athens, Greece. The parallelization of the DE algorithm is performed using the Message Passing Interface (MPI), while artificial neural networks (ANNs) are used as surrogate models. Numerical simulations are performed, which demonstrate that the DE algorithm can be effectively used for the search of the globally optimal model parameters in the GKT model; in fact, the method appears to be promising for the calibration of other similar traffic models as well. en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/en
Date of Item2018-04-16-
Date of Publication2017-
SubjectParallel differential evolutionen
SubjectSurrogate modelsen
SubjectArtificial neural networksen
SubjectMacroscopic traffic flow modelingen
Bibliographic CitationG. A. Strofylas, K. N. Porfyri, I. K. Nikolos, A. I. Delis and M. Papageorgiou, "Calibrating a traffic flow model with parallel differential evolution," in Fifth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering, 2017. doi: 10.4203/ccp.111.26en

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